渤海海冰漂移及其动力影响因子数值模拟研究

渤海作为中国重要的海上枢纽,每年冬季均会发生不同程度水位的海冰灾害,严重威胁区域经济健康发展。为研究渤海海域海冰的生长消融与动态分布过程,本文综合考虑潮流、风场、气温等影响因素,建立渤海海冰三维数学模型,对比分析潮流和风场协同作用,不考虑海冰运动,仅风场作用以及仅潮流作用工况下渤海海冰的运动过程。模拟结果表明:潮流的不对称性会造成海冰漂移,仅潮流作用下的海冰漂移具有明显周期性;渤海三个海湾海冰面积在1月25日—3月3日、1月12日—2月20日与1月16日—2月13日呈现明显周期性变化,对应周期为16、17和18 d;三个海湾仅有风作用时与潮流和风协同作用时的海冰面积变化趋势一致;风对海冰分布有重要影响,海冰所受主要风会对其造成同向输送;渤海海域冬季盛行偏北风,促进渤海海冰从近岸向海漂移,当风向一定时,风速变化引起不同厚度海冰的面积变化及质心位置改变;随着风速增大的加剧,东北风向对辽东湾海冰漂移影响最大,西北风向对渤海湾与莱州湾海冰漂移影响最大。本研究丰富了对渤海海冰演变机理与运动过程的科学认知,可为区域灾害防范与应对提供科技支撑。

湖冰作为极地海冰技术中试基地的探讨

极地海冰技术验证需在不同环境中开展测试,渤海海冰的不稳定性无法保障反复的长期试验时间,寻求利用国内寒区湖泊作为中试基地,解决室内缺少接近极地条件的中试难题。为此,在含章湖、青海湖、乌梁素海和呼和诺尔湖开展湖冰生消、物理性质、力学性质等调查研究,并与南、北极海冰的物理和力学性质进行对比评估。结果表明,含章湖、青海湖、乌梁素海湖和呼和诺尔湖均有时间充裕的冰封期。呼和诺尔湖冰厚最大,超过1 m;乌梁素海冰厚超过50 cm;含章湖和青海湖冰厚相似,最大冰厚超过30 cm。含章湖、青海湖和乌梁素海冰晶体均以柱状冰为主,与极地一年冰晶体结构相似。含章湖和青海湖冰的盐度及密度与北极夏季海冰相似。含章湖冰的单轴压缩强度比极地夏季海冰强度高;乌梁素海湖冰强度与南极普里兹湾固定冰强度相似。研究结果表明国内一些湖泊可作为极地海冰技术验证的中试基地。

孔隙对粒状海冰单轴压缩强度影响的数值模拟研究

强度是影响海冰与结构物相互作用关系的关键性质之一。海冰内部的卤水胞和气泡等孔隙结构对海冰的强度有重要影响。为从细观角度探究冰内孔隙含量、形状和尺寸等海冰结构参数对海冰力学性质的影响,基于离散元方法,建立了包含孔隙的数值海冰模型,模拟粒状冰在平行和垂直冰面方向加载脆性破坏的单轴压缩过程。孔隙尺寸设置为符合均匀分布、标准正态分布和Gamma分布等不同随机分布类型。数值模拟试验结果表明孔隙率是影响海冰强度的主要因素,海冰单轴压缩强度和弹性模量均随孔隙的增加而减小。当压缩应力达到极值时,冰内裂缝迅速扩展。对于圆形孔隙,裂缝主要沿荷载施加方向开展,因此平行冰面方向试样破坏时多表现为大裂缝;对于椭圆形孔隙,裂缝易扩展形成裂缝带。当孔隙率相同时,孔隙尺寸随机分布类型和位置对单轴压缩强度和弹性模量影响不大,但影响冰内裂缝的扩展方式。

基于船基图像的北极冰面融池形态研究

基于中国第12次北极科学考察期间获取的船侧倾斜拍摄海冰图像,研究了北极冰面融池大小和形态参数的空间分布及统计特征。应用自动检测算法将图像划分为水、冰、融池三种表面类别,经倾斜校正后计算每种类别的面积分数及单个融池的大小和形态。结果显示,融池的面积分数随纬度升高先增大后减小,较短的融化时间和较早的再冻结导致高纬度地区融池面积分数一直较低。融池的面积、周长和平均钳测直径随纬度升高具有相似变化规律,均为先增大后减小。不同大小融池面积的频率分布与幂律函数相吻合,对应指数均在1.4~1.8范围内,具有一定相似性。融池的分形维数随纬度升高分布较均匀,而周长与面积比值随纬度变化较大。不同纬度融池边缘凸度与圆形的对应值π相差较大,而各纬度融池圆度平均值为2.39±0.23;融池圆度和凸度与其面积成正比,周长与面积比值则与融池面积成反比,表明融池边缘随着融池面积增加而变得更长更曲折。冰面融池变化引起的平均海表反照率空间差异较大,随着纬度升高在0.24~0.67之间变化,变化率为0.10(°N)^(-1)。本文研究的融池大小和形态参数可为融池演变模拟及遥感反演算法改进提供重要观测依据和地面验证。

MOSAiC现场观测期间海冰厚度季节变化模拟误差分析

北极气候研究多学科漂流观测计划(Multidisciplinary drifting Observatory for the Study of Arctic Climate, MOSAiC)于2019年10月至2020年9月开展,期间获得了变量完整的大气、海洋、海冰厚度及积雪厚度观测,为海冰模式的发展提供了新的契机。本研究利用两个完整观测时段(2019年11月1日至2020年5月7日、2020年6月26日至7月27日)的大气和海洋强迫场,驱动一维海冰柱模式ICEPACK,模拟了MOSAiC期间海冰厚度的季节演变,同海冰厚度观测进行了对比,并诊断分析了海冰厚度模拟误差的原因。结果表明,在冬春季节,模式可以再现海冰厚度增长过程,但由于模式在春季高估了积雪向海冰的转化及对海冰物质平衡的贡献,模拟的春季海冰厚度偏厚。在夏季期间,2种热力学方案及3种融池方案的组合都表明模式高估了海冰表层的消融过程,导致模拟结束阶段的海冰厚度偏薄。我们的研究表明,使用变量完整的MOSAiC大气和海洋强迫场可以诊断目前海冰模式中的问题,为海冰模式的改进奠定基础。

Arctic Sea Ice Variations in the First Half of the 20th Century:A New Reconstruction Based on Hydrometeorological Data

The shrinking Arctic sea-ice area(SIA) in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration(SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used Had ISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in Had ISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km^(2) in March and 1.5 mln km^(2) in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.

基于改进DeepLabV3+模型的海冰提取方法——以北极格陵兰海为例

海冰是全球气候变化的指示剂,北极海冰的变化关系到全球变暖、海平面上升等。针对传统语义分割模型对海冰进行提取时存在细节提取不精确、提取速度慢等问题,构建了一种改进DeepLabV3+的海冰提取方法。首先,将主干网络Xception替换为MobileNetV2,在保证海冰提取精度的同时大幅度降低模型参数量,节约时间;其次,将ASPP改进为DenseASPP,在进行海冰的多尺度特征提取时进一步扩大感受野,获得更为密集的特征;最后,引入坐标注意力机制,同时强化关注通道和空间上的特征,加强海冰边缘细节信息提取。选取北极格陵兰海为实验区,通过对该海域2020–2022年间冬季的10景Sentinel-1A双极化SAR影像进行处理、标注之后形成数据集进行实验,对比U-Net、PSPNet和DeepLabV3+等经典模型。结果表明:本文方法的m IoU达到了88.46%,mPA达到了94.16%。相较于传统DeepLabV3+,mIoU提高了2.35%,mPA提高了2.90%,参数量和GFLOPs分别减少了45.08 M和106.01 G,同时训练模型时间和提取海冰时间分别减少了68%和30%。对比U-Net、PSPNet等模型,同样取得了最优结果。与其他模型相比,本文新构建的模型对海冰特征的学习能力更强,能获取更多海冰细节信息,并大幅度节约用时,能够为研究全球变暖环境下的海冰退化监测问题提供技术支持。

黑碳对北冰洋积雪和海冰影响的模拟研究

当黑碳沉降到冰雪表面时,可使冰雪表面反照率降低,对短波辐射的吸收增加,黑碳的变化对海冰融化过程的影响值得研究。本文利用CICE海冰模式进行数值模拟,并定量分析北冰洋冰雪中黑碳造成的影响。研究表明,在不同黑碳数据源的强迫下,1980-2014年间,模拟结果给出的夏季北冰洋反照率平均下降为0.82%~1.71%,最终造成海冰面积下降了0.97%~1.93%,而在巴伦支海、喀拉海以及拉普捷夫海,夏季黑碳造成的海冰面积下降约为北冰洋整体的2-3倍。不同黑碳沉降强迫下的模拟结果均显示,1980-1995年,北冰洋区域黑碳对反照率的影响呈现减小趋势,但在1996-2014年,黑碳影响转为增加趋势。在低纬度海区,由于海冰的消退,黑碳的辐射效应呈现减小趋势,而在高纬度海区,由于多年冰内黑碳的累积效应,黑碳的辐射影响呈现增强效应。

北极高分辨率海冰冰间水道模拟评估分析

冰间水道只占北极冰区的1%~10%,但在海洋和大气之间的能量和水汽交换中起着重要作用。目前对冰间水道数值模拟结果的分析主要集中在水道出现频率的空间分布和水道面积占比的时空变化,而有关水道形态特征(长、宽和走向)模拟结果的分析较少。本文基于采用黏塑性流变学的高分辨率(2 km)冰海耦合模式模拟的海冰厚度来提取冰间水道,并利用3种MODIS冰间水道遥感产品进行形态特征的对比分析。分析结果显示,在波弗特海,模拟水道出现频率的空间分布与WH2015和H2019两种遥感产品基本一致;模拟结果中宽度大于6 km的水道数量概率密度和总长度符合遥感产品呈现的幂律分布规律,对2~4 km窄水道的分布受模式可解析能力限制本研究模式分辨率尚无法正确估计,存在低估;模拟水道总长度与遥感反演的结果在1月和3月相关性较好,但无法再现遥感产品中2月和4月的明显变化趋势;模拟水道和遥感产品总体走向一致,二者都显示,加拿大群岛以北和波弗特海东南部沿岸区域水道走向几乎与海岸线和冰速方向平行,模拟水道受陆地阻隔的影响更大,在波弗特海中部冰间水道和冰速发生转向的位置不一致。本研究揭示了当前高分辨率海冰模式对不同冰间水道形态特征的模拟能力的符合程度及不足之处,将有助于进一步的模式改进。

基于多源卫星雷达高度计的南极海冰厚度估算

南极海冰是极地气候系统的重要组成部分,对全球气候有着深远影响。海冰厚度作为海冰的关键属性之一,了解其时空分布特征和变化趋势对理解和预估气候变化进程具有重要意义。然而,目前对于南极海冰厚度的监测局限在时空分布有限的实地观测和短时间序列的卫星观测阶段,长时间序列的冰厚数据仍然缺失。针对这一问题,基于时空连续的卫星雷达高度计Envisat和CryoSat-2,构建了一致性较高的雷达干舷高度数据,定量估算了高度计雷达信号在积雪中的穿透深度,发展了一种适用于南极海冰的厚度遥感估算方法。估算的冰厚与Australian Antarctic Data Centre实测数据的平均绝对偏差约为0.28 m;与ICESat激光雷达高度计冰厚的平均绝对偏差约为0.65 m,相关系数为0.71,一致性较高。2002–2023年南极海冰厚度时空变化分析结果表明,厚冰主要分布于西威德尔海和别林斯高晋海/阿蒙森海,其他海域海冰相对较薄。南极海冰厚度在2011年之前呈现轻微降低趋势,在2011年后加速降低(-0.03 m/a)。南极海冰厚度的分布和变化趋势存在明显的季节和区域特征。

CMIP6计划中我国地球气候系统模式北极海冰空间分布的模拟评估

世界气候研究计划(WCRP)正在组织实施第6次国际耦合模式比较计划(CMIP6)。本文选取了参加CMIP6的9个中国大陆地球气候系统模式的北极海冰输出结果与同时段海冰遥感观测数据进行比较,评估了各个模式1980年至2014年北极海冰密集度和其长期趋势的空间分布。研究表明,所有的模式都可以较好地模拟出3月北极海盆海冰的分布情况,误差主要分布在海冰边缘地区,其中鄂霍茨克海的中部以及巴伦支海地区误差最大,最高值可达90%。与3月相比,模式对9月海冰空间分布的模拟效果不佳,在北极海盆地区以及海冰边缘地区均存在15%以上的误差。在海冰密集度长期趋势空间分布方面,3月,9个模式总体高估了海冰下降区的海域面积,在鄂霍茨克海、巴伦支海以及格陵兰海北部海域为模式误差大值区(大于50%)。模式在模拟9月海冰下降趋势的区域及量级上较3月都有更大的偏差。另外,9个模式对海冰密集度多年平均季节变化的模拟能力与其对长期趋势的模拟能力有一定关联,对海冰密集度季节变化模拟准确的模式,其海冰长期趋势的模拟也较接近观测。海冰分量模式中参数化方案的改进可以明显提高模式的模拟能力。

1979-2022年北极海冰变化及影响因素分析

海冰是海洋-大气交互系统的重要组成部分,是全球气候变化的指示器。深入分析海冰演变规律,探索全球气候变化与海冰范围之间的关联,对应对和减缓全球气候变化具有重要的理论意义。本文以北极海冰为研究对象,深入分析了1979-2022年北极海冰范围的季节、年际和年代际变化特征,并构建向量自回归模型(Vector AutoRegressive Model,VAR)检验全球平均气温、大气CO_(2)浓度与北极海冰范围之间的因果关系,并分析影响程度。结果表明:(1)北极海冰范围季节变化特征明显,一般在3月达到最大,9月达到最小,这主要与太阳辐射的年变化周期相关;(2)1979-2022年,北极海冰范围总体呈减小趋势,年变化量为5.3万km^(2);(3)北极海冰范围在年代际尺度上逐渐减小,2000-2009年,北极海冰范围较上一个十年减小最多(减少67万km^(2));(4)全球平均气温和大气CO_(2)浓度均对北极海冰范围变化造成了显著影响;(5)全球平均气温和大气CO_(2)浓度与北极海冰范围均有显著负相关关系,相关系数分别为-0.92和-0.95。

基于Sentinel-1的辽东湾海冰冰情监测

辽东湾是渤海的三大海湾之一,在每年冬季经常会遭受大面积海冰灾害影响。本文选取2015年至2023年Sentinel-1 A/B遥感数据,对照Landsat-8数据采用灰度共生矩阵提取SAR影像纹理特征,对比了神经网络、支持向量机以及最大似然分类三种分类方法的精度;统计2015年至2023年辽东湾海冰冰情信息,研究了气温、海表面温度、盐度与海冰的相关关系和相互影响。研究结果表明:对于Sentinel-1 A/B数据,最大似然分类方法精度最高,分类精度达到82.75%;辽东湾海冰通常在12月底或者1月初开始发育,辽东湾结冰顺序为由北向南、由近岸向远岸,消融时顺序为由西向东、由远岸向近岸;辽东湾海冰与气温的Pearson相关系数为-0.502(P<0.01),与海表面温度的Pearson相关系数为-0.553(P<0.01),说明辽东湾冰情与气温、海表面温度为显著负相关,海表面温度对海冰的形成、发育及消融影响更大。此外,盐度对于海冰的影响存在一定的滞后性。

Four-to Six-Year Periodic Variation of Arctic Sea-Ice Extent and Its Three Main Driving Factors

Besides the rapid retreating trend of Arctic sea-ice extent(SIE),this study found the most outstanding low-frequency variation of SIE to be a 4-6-year periodic variation.Using a clustering analysis algorithm,the SIE in most ice-covered regions was clustered into two special regions:Region-1 around the Barents Sea and Region-2 around the Canadian Basin,which were located on either side of the Arctic Transpolar Drift.Clear 4-6-year periodic variation in these two regions was identified using a novel method called“running linear fitting algorithm”.The rate of temporal variation of the Arctic SIE was related to three driving factors:the regional air temperature,the sea-ice areal flux across the Arctic Transpolar Drift,and the divergence of sea-ice drift.The 4-6-year periodic variation was found to have always been present since 1979,but the SIE responded to different factors under heavy and light ice conditions divided by the year 2005.The joint contribution of the three factors to SIE variation exceeded 83%and 59%in the two regions,respectively,remarkably reflecting their dynamic mechanism.It is proven that the process of El Niño-Southern Oscillation(ENSO)is closely associated with the three factors,being the fundamental source of the 4-6-year periodic variations of Arctic SIE.

Joint Probability Analysis and Prediction of Sea Ice Conditions in Liaodong Bay

Sea ice conditions in Liaodong Bay of China are often described by sea ice grades,which classify annual sea ice conditions based on the annual maximum sea ice thickness(AM-SIT)and annual maximum floating ice extent(AM-FIE).The joint probability distribution of AM-SIT and AM-FIE was established on the basis of their data pairs from 1949/1950 to 2019/2020 in Liaodong Bay.The joint intensity index of the sea ice condition in the current year is calculated,and the joint classification criteria of the sea ice grades in past years are established on the basis of the joint intensity index series.A comparison of the joint criteria with the 1973 and 2022 criteria revealed that the joint criteria of the sea ice grade match well,and the joint intensity index can be used to quantify the sea ice condition over the years.A time series analysis of the sea ice grades and the joint intensity index sequences based on the joint criteria are then performed.Results show a decreasing trend of the sea ice condition from 1949/1950 to 2019/2020,a mutation in 1990/1991,and a period of approximately 91 years of the sea ice condition.In addition,the Gray-Markov model(GMM)is applied to predict the joint sea ice grade and the joint intensity index of the sea ice condition series in future years,and the error between the results and the actual sea ice condition in 2020/2021 is small.

Numerical Simulation of Multiple Collisions Between Ice Ridge and Polar Ship

Ships navigating in ice-covered regions will inevitably collide with ice ridges.Compared to other ice bodies,ice ridges exhibit more complicated mechanical behaviors due to the scale and structure characteristics.In this paper,nonlinear finite element method is used to investigate the interaction between a polar ship and an ice ridge.The ice ridge is modelled as elastic-plastic material based on Drucker-Prager yield function,with the consideration of the influence of cohesion,friction angle and material hardening.The material model is developed in LS-DYNA and solved using semi-implicit mapping algorithm.The stress distribution of ice ridge and ship,and the ice load history are evaluated through the simulation of multiple collisions.In addition,parametric analysis is performed to investigate the influence of ridge thickness and impact velocity on the ice load and energy absorption.

Spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice in multiple dimensions during 1979 to 2020

Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change.The rapid changes in Arctic sea ice have been widely concerned.However,the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series.In this study,the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data.The results indicated that Arctic sea ice was declining at a remarkably high rate of–5.4×10^(4) km^(2)/a in sea ice area(SIA)and–2.2 cm/a in sea ice thickness(SIT)during 1979 to 2020,and the reduction of SIA and SIT was the largest in summer and the smallest in winter.Spatially,compared with other sub-regions,SIA showed a sharper declining trend in the Barents Sea,Kara Sea,and East Siberian Sea,while SIT presented a larger downward trend in the northern Canadian Archipelago,northern Greenland,and the East Siberian Sea.Regarding to the seasonal trend of sea ice on sub-region scale,the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons,especially in summer and winter,i.e.,the sub-regions linked to the open ocean exhibited a higher decline rate in winter;however,the other sub-regions blocked by the coastlines presented a greater decline rate in summer.For SIT,the sub-regions such as the Beaufort Sea,East Siberian Sea,Chukchi Sea,Central Arctic,and Canadian Archipelago always showed a higher downward rate in all seasons.Furthermore,a striking freeze-thaw asymmetry of Arctic sea ice was also detected.Comparing sea ice changes in different dimensions,sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension.The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period,and the rate of sea ice melting was 0.3×10^(4) km^(2)/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions,respectively.Moreover,there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004,respectively,in the horizontal/vertical dimension.

Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario

The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.

Climate prediction of the seasonal sea-ice early melt onset in the Bering Sea

基于大尺度环流异常对海冰消融的影响过程,本文采用年际增量预测方法研制了白令海季节性海冰早期消融开始日期(EMO)的统计预测模型.预测模型选取了3个具有明确物理意义的预测因子:1月波弗特高压,前期11月东西伯利亚地区海平面气压,以及11月东欧平原积雪覆盖率。1月波弗特高压可以通过海气相互作用影响白令海地区海温异常,该海温异常能够从1月持续到3月,进而影响白令海EMO.11月东西伯利亚地区海平面气压与11月至次年2月北太平洋中纬度东部海温密切相关。伴随着北太平洋中纬度东部冷海温异常的出现,白令海地区会出现暖海温异常,进而导致白令海海冰范围减少,EMO较晚.1月北极偶极子异常是11月东欧平原积雪覆盖率影响次年白令海EMO的桥梁之一.1981-2022年的交叉检验结果表明:统计模型对白令海EMO具有较好的预测能力,预测与观测的EMO之间时间相关系数达到了0.45,超过了99%的置信水平.统计模型对白令海EMO正常年份和异常年份的预测准确率分别为60%和41%.

Changes in area fraction of sediment-laden sea ice in the Arctic Ocean during 2000 to 2021

Sediment-laden sea ice plays an important role in Arctic sediment transport and biogeochemical cycles,as well as the shortwave radiation budget and melt onset of ice surface.However,at present,there is a lack of efficient observation approach from both space and in situ for the coverage of Arctic sediment-laden sea ice.Thus,both spatial distribution and long-term changes in area fraction of such ice floes are still unclear.This study proposes a new classification method to extract Arctic sediment-laden sea ice on the basic of the difference in spectral characteristics between sediment-laden sea ice and clean sea ice in the visible band using the MOD09A1 data with the resolution of 500 m,and obtains its area fraction over the pan Arctic Ocean during 2000−2021.Compared with Landsat-8 true color verification images with a resolution of 30 m,the overall accuracy of our classification method is 92.3%,and the Kappa coefficient is 0.84.The impact of clouds on the results of recognition and spatiotemporal changes of sediment-laden sea ice is relatively small from June to July,compared to that in May or August.Spatially,sediment-laden sea ice mostly appears over the marginal seas of the Arctic Ocean,especially the continental shelf of Chukchi Sea and the Siberian seas.Associated with the retreat of Arctic sea ice extent,the total area of sediment-laden sea ice in June-July also shows a significant decreasing trend of 8.99×10^(4) km^(2) per year.The occurrence of sediment-laden sea ice over the Arctic Ocean in June-July leads to the reduce of surface albedo over the ice-covered ocean by 14.1%.This study will help thoroughly understanding of the role of sediment-laden sea ice in the evolution of Arctic climate system and marine ecological environment,as well as the heat budget and mass balance of sea ice itself.